likheet / mini

Fraud Detection using Machine Learning in Python

Overview

This documentation details the comprehensive steps and methodologies used to build a predictive algorithm for fraud detection using a training dataset. The objective is to create an accurate model to identify fraudulent transactions. The process is guided by Local Interpretable Model-agnostic Explanations (LIME) principles to ensure clarity and accessibility.

Table of Contents

1. Environment Set-up

   • Importing Libraries
   • Loading the Data

2. Initial Diagnostics

   • Data Overview
   • Descriptive Statistics
   • Target Variable Analysis
   • Predictor Variable Analysis
   • Correlation Matrix

3. Data Processing

   • Handling Missing Values
   • Managing Outliers
   • Removing Duplicate Observations

4. Exploratory Data Analysis

   • Analyzing Transaction Amounts
   • Time-Based Analysis of Fraud

5. Class Imbalance Solutions

   • SMOTE (Synthetic Minority Oversampling Technique)
   • Near-Miss Algorithm
   • Combined Sampling

6. Dimensionality Reduction

   • Principal Component Analysis (PCA)
   • Singular Value Decomposition (SVD)
   • Linear Discriminant Analysis (LDA)

7. Machine Learning Set-up

   • Train-Test Split
   • Cross-Validation

8. Machine Learning Models

   • Simple Models
   • Ensemble Methods

9. Final Recommendation

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1. Environment Set-up

Importing Libraries

• Libraries for data manipulation, visualization, statistical methods, sampling methods, model selection, dimensionality reduction, simple ML models, and ensemble learning are imported.
• Set a seed for reproducibility.

Loading the Data

• Load the dataset using pandas to read the CSV file containing the transaction data.

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2. Initial Diagnostics

Data Overview

• Get a preliminary understanding of the dataset using functions like df.info() to check the structure and types of the data.

Descriptive Statistics

• Use df.describe() to generate summary statistics of the dataset.

Target Variable Analysis

• Analyze the distribution of the target variable to understand the class imbalance.

Predictor Variable Analysis

• Examine the basic statistics and distribution of key predictors like the transaction amount.
• Use histograms and log-scale transformations to visualize data distributions.

Correlation Matrix

• Create a correlation matrix to identify relationships between variables.
• Visualize correlations using a heatmap.

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3. Data Processing

Handling Missing Values

• Identify columns with missing values.
• Impute missing values using median or mode, depending on the data type.

Managing Outliers

• Detect outliers using statistical methods like the z-score.
• Visualize outliers using boxplots and decide on handling strategies.

Removing Duplicate Observations

• Identify and remove duplicate observations to ensure data quality.

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4. Exploratory Data Analysis

Analyzing Transaction Amounts

• Split the dataset into fraudulent and non-fraudulent transactions.
• Use histograms to compare the distribution of transaction amounts for both classes.
• Apply log transformation for better visualization.

Time-Based Analysis of Fraud

• Plot transaction amounts over time to identify any time-based patterns in fraudulent activities.

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5. Class Imbalance Solutions

SMOTE (Synthetic Minority Oversampling Technique)

• SMOTE is used to oversample the minority class by creating synthetic examples.

Near-Miss Algorithm

• This undersampling technique selects examples from the majority class that are close to the minority class examples.

Combined Sampling

• Apply both oversampling and undersampling techniques to balance the dataset.

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6. Dimensionality Reduction

Principal Component Analysis (PCA)

• Reduce the number of features while retaining most of the variance in the data.

Singular Value Decomposition (SVD)

• Decompose the data matrix into singular vectors and values for dimensionality reduction.

Linear Discriminant Analysis (LDA)

• Use LDA to project data in a way that maximizes class separability.

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7. Machine Learning Set-up

Train-Test Split

• Split the data into training and testing sets to evaluate model performance.

Cross-Validation

• Implement cross-validation techniques to ensure robust model evaluation.

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8. Machine Learning Models

Simple Models

• Logistic Regression
• k-Nearest Neighbors (k-NN)
• Decision Tree
• Stochastic Gradient Descent (SGD)

Ensemble Methods

• Random Forest
• Stochastic Gradient Boosting
• Stacking

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9. Final Recommendation

• Summarize findings and recommend the best-performing model based on evaluation metrics.